Zusammenfassung
Automated driving is a key technology for the future of transportation. There are several motivations to develop automated vehicles. First and foremost, it promises to reduce the number of traffic accidents. Figure 1 shows the accidents recorded by the German police over the past years ([1]) ranging back to 1960.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Literatur
Polizeilich erfasste Unfälle, Statistisches Bundesamt (Destatis), 2019. In www.destatis.de (Thematische Recherche: Zahlen & Fakten – Wirtschaftsbereiche – Transport & Verkehr – Verkehrsunfälle – Dokumentart: Tabelle). Abrufdatum: 06.02.2019
C. Wissing, T. Nattermann, K. H. Glander and T. Bertram, “Interaction-Aware Long-term Driving Situation Prediction,” in IEEE International Conference on Intelligent Transportation Systems 2018
S. Lefevre, D. Vasquez, and C. Laugier, “A survey on motion prediction and risk assessment for intelligent vehicles,” ROBOMECH Journal, vol. 1, no. 1, p. 1, Jul 2014.
T. Gindele, S. Brechtel, and R. Dillmann, “A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments,” in 13th International IEEE Conference on Intelligent Transportation Systems, Sept 2010, pp. 1625–1631.
M. Schreier, V. Willert, and J. Adamy, “An integrated approach to maneuverbased trajectory prediction and criticality assessment in arbitrary road environments,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 10, pp. 2751–2766, Oct 2016.
M. Bahram, C. Hubmann, A. Lawitzky, M. Aeberhard, and D. Wollherr, “A combined model- and learning-based framework for interaction-aware maneuver prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 6, pp. 1538–1550, June 2016.
M. Koschi and M. Althoff, “Interaction-aware occupancy prediction of road vehicles,” in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Oct 2017, pp. 1–8.
D. S. Gonzalez, V. Romero-Cano, J. S. Dibangoye, and C. Laugier, “Interaction-aware driver maneuver inference in highways using realistic driver models,” in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Oct 2017, pp. 1–8.
M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simulations,” Phys. Rev. E, vol. 62, pp. 1805–1824, Aug 2000.
A. Kesting, M. Treiber, and D. Helbing, “General lane-changing model mobil for car-following models,” Transportation Research Record, vol. 1999, no. 1, pp. 86–94, 2007.
M. Schmidt, C. Manna, J. Braun, C. Wissing, M. Mohamed and T. Bertram, “An Interaction-Aware Lane Change Behavior Planner for Automated Vehicles on Highways based on Polygon Clipping,” in IEEE Robotics and Automation Letters
M. Werling, J. Ziegler, S. Kammel and S. Thrun, “Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenét Frame,” in IEEE International Conference on Robotics and Automation 2010
C. Richter, A. Bry and N. Roy, “Polynomial Trajectory Planning for Aggressive Quadrotor Flight in Dense Indoor Environments,” in International Symposium ISRR 2016
C. Richter, A. Bry and N. Roy, “Polynomial trajectory planning for quadrotor flight,” in IEEE International Conference on Robotics and Automation 2013
B. Paden, M. Cap, S. Z. Yong, D. Yershov and E. Frazzoli, “A survey of motion planning and control techniques for self-driving urban vehicles,” in IEEE Transactions on Intelligent Vehicles, vol. 1, no. 1, pp. 33–55, 2016.
D. Gonzalez, J. Perez, V. Milanes, and F. Nashashibi, “A review of motion planning techniques for automated vehicles,” in IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 4, pp. 1135–1145, 2016
S. M. LaValle, “Rapidly-exploring random trees: A new tool for path planning,” Ph.D. dissertation, Iowa State University, Oct. 1998
M. McNaughton, C. Urmson, J. M. Dolan, and J.-W. Lee, “Motion planning for autonomous driving with a conformal spatiotemporal lattice,” in IEEE International Conference on Robotics and Automation (ICRA), pp. 4889–4895, 2011.
J. Ziegler, P. Bender, T. Dang, and C. Stiller, “Trajectory planning for bertha — a local, continuous method,” in IEEE Intelligent Vehicles Symposium (IV), pp. 450–457, 2014.
C. Götte, M. Keller, T. Nattermann, C. Haß, K.-H. Glander, and T. Bertram, “Spline-based motion planning for automated driving,” in Proceedings of the 20th IFAC World Congress, pp. 9444–9449, 2017.
C. Lienke, M. Keller, K.-H. Glander, and T. Bertram, “An ad-hoc samplingbased planner for on-road automated driving,” in IEEE International Conference on Intelligent Transportation Systems (ITSC), pp. 2371–2376, 2018.
C. Wissing, T. Nattermann, K.-H. Glander, A. Seewald, and T. Bertram, “Environment simulation for the development, evaluation and verification of underlying algorithms for automated driving,” in AmE 2016 – Automotive meets Electronics; 7th GMM-Symposium, pp. 9–14, 2016.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Lienke, C. et al. (2020). Core components of automated driving – algorithms for situation analysis, decision-making, and trajectory planning. In: Bertram, T. (eds) Automatisiertes Fahren 2019. Proceedings. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-27990-5_17
Download citation
DOI: https://doi.org/10.1007/978-3-658-27990-5_17
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-27989-9
Online ISBN: 978-3-658-27990-5
eBook Packages: Computer Science and Engineering (German Language)